CT is currently the gold standard in radiation therapy treatment planning. MRI provides a number of advantages over CT, including improved accuracy of target delineation, reduced radiation exposure, and simplified clinical workflow. There are two major technical hurdles that are impeding the clinical adoption of MRI-based radiation treatment planning: (1) geometric distortion, and (2) lack of electron density information. The goal of this project is to develop novel image analysis and computational tools to enable MRI-based radiation treatment planning. We hypothesize that accurate patient geometry and electron density information can be derived from MRI if the appropriate MR image acquisition, reconstruction, and analysis methods are applied. In Aim 1, we will improve the geometric accuracy of MRI by minimizing system-level and patient- specific distortions. To maintain sufficient system-level accuracy, we will perform comprehensive machine- specific calibrations and ongoing quality assurance procedures. To correct patient-induced distortions, we will develop novel computational tools to derive a detailed magnetic field distortion map based on physical principles, which is used to correct susceptibility-induced spatial distortions. In Aim 2, we will develop a unifying Bayesian method for quantitative electron density mapping, by combining the complementary intensity and geometry information. By utilizing multiple patient atlases and panoramic, multi-parametric MRI with differential contrast, we will apply machine learning techniques to encode the information given by intensity and geometry into two conditional probability density functions. These will be combined into one unifying posterior probability density function, which provides the optimal electron density on a continuous scale. In Aim 3, we will clinically evaluate the geometric and dosimetric accuracy of MRI for treatment planning in terms of 3 primary end points: (1) organ contours, (2) patient setup based on reference images, and (3) 3D dose distributions (both photon and proton), using CT as the ground truth. These evaluations will be conducted through patient studies at multiple disease sites, including brain, head and neck, and prostate. Success of the project will afford distortion-free MRI with reliable, quantitative electron density information. This will pave the way for MRI-based radiation treatment planning, leading to an improved accuracy in the overall radiation therapy process. It will streamline the treatment workflow for the MRI-guided radiation delivery systems under active development. With minimal modification, the proposed techniques can be applied to MR-based PET attenuation correction in PET/MR imaging. More broadly, the unifying Bayesian formalism can be used to improve current imaging biomarkers by integrating a wide variety of disparate information including anatomical and functional imaging such as perfusion/diffusion-weighted imaging and MR spectroscopic imaging. It will facilitate the incorporation of multimodality MRI into the entire process of cancer management: diagnosis, staging, radiation treatment planning, and treatment response assessment.